A novel Blood Pressure estimation method combing Pulse Wave Transit Time model and neural network model.

Blood Pressure (BP) measurement can assist doctors to assess patients' cardiovascular status and diagnose heart diseases. Pulse Wave Transit Time (PWTT) model is one frequently used BP estimation method to monitor BP continuously in clinics. However, individual variations may influence the measurement accuracy of PWTT model. Focusing on above promble, this paper proposes a novel BP estimation method combining a classical PWTT model and a neural network model. The novel method is composed of five steps: signal pre-processing, feature extraction, initial PWTT model selection, model correction by neural network model, and final PWTT model identification. A validation experiment based on 10 patients from Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) database showed that the BP estimation results by our method had a minimum mean of error readout value 5 mmHg with a standard deviation of error readout value ±8mmHg. As a result, both the diastolic blood pressure and systolic blood pressure estimation by our method can meet clinical requirements.

Disrupted network topology in patients with idiopathic rapid eye movement sleep behavior disorder / 中华核医学与分子影像杂志

Objective To explore the topological abnormality of brain metabolic network in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and compare it with the topology of brain metabolic network in patients with Parkinson's disease (PD).Methods The 18F-fluorodeoxyglucose (FDG) PET brain images of 19 patients with iRBD diagnosed with polysomnography (PSG) (iRBD group;15 males,4 females,average age:64.9 years),19 patients with PD (PD group;12 males,7 females,average age:62.2 years) and 19 gender and age-matched healthy controls (HC group;15 males,4 females,average age:63.1 years) in Huashan Hospital from September 2014 to June 2015 were retrospectively analyzed.According to the complex brain network method based on graph theory,the brain metabolic networks of each group was constructed and the network parameters (clustering coefficient,characteristic path length,local efficiency,global efficiency and small-world property,etc) were evaluated quantitatively.The 500 times non-parametric permutation test was used to determine the differences in network parameters between groups.Results The brain metabolic networks of iRBD group and PD group both had abnormal topological structure,which showed that the characteristic path length (for example,when sparsity =34％,HC vs iRBD vs PD groups:1.517 vs 1.552 vs 1.561) and local efficiency (for example,when sparsity=30％,HC vs iRBD vs PD groups:0.802 vs 0.824 vs 0.831) were significantly increased (both P＜0.05),the global efficiency (for example,when sparsity =36％,HC vs iRBD vs PD groups:0.672 vs 0.658 vs 0.656) was significantly decreased (P＜0.05).The topology was more aggravated in PD group compared with that in iRBD group.Conclusion The graph-based complex brain network analysis can reveal the abnormal topological structure of the brain metabolic network in which iRBD progresses to PD.

Disrupted network topology in patients with idiopathic rapid eye movement sleep behavior disorder / 中华核医学与分子影像杂志

Objective@#To explore the topological abnormality of brain metabolic network in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and compare it with the topology of brain metabolic network in patients with Parkinson′s disease (PD).@*Methods@#The 18F-fluorodeoxyglucose (FDG) PET brain images of 19 patients with iRBD diagnosed with polysomnography (PSG) (iRBD group; 15 males, 4 females, average age: 64.9 years), 19 patients with PD (PD group; 12 males, 7 females, average age: 62.2 years) and 19 gender and age-matched healthy controls (HC group; 15 males, 4 females, average age: 63.1 years) in Huashan Hospital from September 2014 to June 2015 were retrospectively analyzed. According to the complex brain network method based on graph theory, the brain metabolic networks of each group was constructed and the network parameters (clustering coefficient, characteristic path length, local efficiency, global efficiency and small-world property, etc) were evaluated quantitatively. The 500 times non-parametric permutation test was used to determine the differences in network parameters between groups.@*Results@#The brain metabolic networks of iRBD group and PD group both had abnormal topological structure, which showed that the characteristic path length (for example, when sparsity=34%, HC vs iRBD vs PD groups: 1.517 vs 1.552 vs 1.561) and local efficiency (for example, when sparsity=30%, HC vs iRBD vs PD groups: 0.802 vs 0.824 vs 0.831) were significantly increased (both P<0.05), the global efficiency (for example, when sparsity=36%, HC vs iRBD vs PD groups: 0.672 vs 0.658 vs 0.656) was significantly decreased (P<0.05). The topology was more aggravated in PD group compared with that in iRBD group.@*Conclusion@#The graph-based complex brain network analysis can reveal the abnormal topological structure of the brain metabolic network in which iRBD progresses to PD.

Wearable Device for Non-Invasive Continuously Blood Pressure Monitoring / 中国医疗器械杂志

In aging society the development of non-invasive continuously blood pressure monitors which are suitable for homes, communities and nursing homes has a wide range of applications. This paper proposes a non-invasive continuously blood pressure monitoring based on wearable device which uses MSP430F5529 as the central processor. The design is divided into signal acquisition module, central control module, display module, power supply module and host computer module. The experimental results showed that DBP (375/390, 96.15%) and SBP estimation values (377/390, 96.67%) are in 95% confidence interval, which means our design passes Bland-Altman test with high accuracy and stability.